Results from CBS data, based on classification model.

Across both datasets, we find supportive results, suggesting that citizens’ evaluations of public services (education, health, policing, and transportation) are colored by their partisan match/mismatch with the governing coalition.

To compensate for the lack of political indicators in CBS dataset, we applied a novel Machine-Learning model to classify partisanship (two-bloc voting) based on demographic and geographic information (achieving ~80% accuracy).

Using Israel’s two full government changes in 2021 and 2022 as a test case, we conduct two pre-registered studies to examine the shift in partisan evaluations of government services based on affiliation with the governing coalition.

📢 New publication in Political Behavior With @acavari.bsky.social and @Liot_Shvarts 📢
Polarization and Partisan Bias in Citizens' Evaluations of Public Services
t.co/kJSR0VDzCq

@polbehavior.bsky.social